Top AI & Machine Learning Jobs

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

The role involves leading AI research projects, building AI foundation models, collaborating with engineering teams, and mentoring applied scientists to enhance customer interactions through AI.

Lead production of machine learning applications, provide technical expertise, and mentor engineers to drive ML model delivery at scale.

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

Lead a team to design and build AI-native engineering harnesses and agentic workflow orchestration for governed model use. Implement typed contracts, observability, CI/CD integration, evaluation gates, developer tooling, and cloud-native services to enable secure, auditable, and repeatable AI-assisted software delivery across the enterprise.

The role involves developing AI foundation models, collaborating with cross-functional teams, and conducting applied research to enhance customer experiences using advanced AI techniques.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

Build and deploy NLP and machine learning models end-to-end, leveraging open-source LLMs, RAG, and evaluation frameworks. Partner with engineers, analysts, and product managers to deliver AI-powered HR tools, using Python, SQL, AWS, LangChain, Hugging Face, VectorDBs, and deep learning frameworks to inform people strategy and talent decisions.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.